def write_netcdf(filename, df, info=None):
# FIXME: still a lot of issues here
if info is None:
info = {}
f = NetCDFFile(filename, "w")
f.createDimension("_2_byte_string", 2)
f.createDimension("_4_byte_string", 4)
f.createDimension("_8_byte_string", 8)
f.createDimension("_16_byte_string", 16)
f.createDimension("_32_byte_string", 32)
f.createDimension("_64_byte_string", 64)
f.createDimension("_128_byte_string", 128)
f.createDimension("_255_byte_string", 255)
f.createDimension("error_number", 1)
f.flush()
f.dataset_completeness = "C1" # TODO: save peaks too? ('C1+C2')
f.netcdf_revision = "2.3.2"
f.languages = "English"
f.flush()
f.experiment_title = info.get("name", " ")
f.operator_name = info.get("operator", " ")
# TODO: wrong format for injection_date_time_stamp
f.injection_date_time_stamp = info.get("date", " ")
f.company_method_id = info.get("method", " ")
f.sample_name = info.get("sample", " ")
f.flush()
f.createDimension("scan_number", len(df.index))
v = f.createVariable("scan_acquisition_time", ">d", ("scan_number",))
v[:] = df.index.astype("d")
v = f.createVariable("total_intensity", ">d", ("scan_number",))
v[:] = df.values.sum(axis=1).astype("d")
v = f.createVariable("point_count", ">i", ("scan_number",))
v[:] = np.sum(df.values != 0, axis=1).astype("i")
f.flush()
f.createDimension("point_number", np.sum(df.values != 0))
stretch_t = np.resize(df.index, df.values.T.shape).T
v = f.createVariable("mass_values", ">f", ("point_number",))
v[:] = stretch_t[df.values != 0]
v = f.createVariable("intensity_values", ">f", ("point_number",))
v[:] = df.values[df.values != 0]
f.close()
def write_netcdf(filename, df, info=None):
# FIXME: still a lot of issues here
if info is None:
info = {}
f = NetCDFFile(filename, 'w', version=1)
f.createDimension('_2_byte_string', 2)
f.createDimension('_4_byte_string', 4)
f.createDimension('_8_byte_string', 8)
f.createDimension('_16_byte_string', 16)
f.createDimension('_32_byte_string', 32)
f.createDimension('_64_byte_string', 64)
f.createDimension('_128_byte_string', 128)
f.createDimension('_255_byte_string', 255)
f.createDimension('error_number', 1)
f.flush()
# TODO: check that these do anything
f.createDimension('instrument_number', 1)
f.createDimension('range', 2)
f.flush()
f.dataset_completeness = 'C1' # TODO: save peaks too? ('C1+C2')
f.aia_template_revision = '1.0'
f.ms_template_revision = '1.0.1'
f.netcdf_revision = '2.3.2'
f.languages = 'English'
f.administrative_comments = 'none'
f.dataset_origin = 'none'
f.dataset_owner = ' '
f.dataset_date_time_stamp = ' '
f.flush()
f.company_method_name = ' '
f.pre_experiment_program_name = ' '
f.post_experiment_program_name = ' '
f.source_file_reference = ' '
f.experiment_title = info.get('name', ' ')
f.operator_name = info.get('operator', ' ')
# TODO: wrong format for injection_date_time_stamp
# example: 20141027123030-0500
f.injection_date_time_stamp = info.get('date', ' ')
f.company_method_id = info.get('method', ' ')
f.sample_name = info.get('sample', ' ')
f.flush()
f.sample_id_comments = 'none'
f.sample_id = 'none'
f.sample_name = 'none'
f.sample_type = 'none'
f.sample_injection_volume = 'none'
f.sample_amount = 'none'
f.flush()
f.retention_unit = 'Seconds'
# TODO: need to merge this back into access method
if hasattr(df.values, 'todense'):
scan_locs = (df.values != 0).todense()
else:
scan_locs = df.values != 0
f.createVariable('error_log', 'c', ('error_number', '_64_byte_string'))
f.createDimension('scan_number', len(df.index))
v = f.createVariable('scan_acquisition_time', '>d', ('scan_number',))
v[:] = 60. * df.index.astype('d')
v = f.createVariable('total_intensity', '>d', ('scan_number',))
v[:] = df.values.sum(axis=1).astype('d').flatten()
v = f.createVariable('point_count', '>i', ('scan_number',))
v[:] = np.sum(scan_locs, axis=1).astype('i').flatten()
f.flush()
f.createDimension('point_number', np.sum(scan_locs))
stretch_t = np.resize(df.index, df.values.T.shape).T
v = f.createVariable('mass_values', '>f', ('point_number',))
v[:] = stretch_t[scan_locs]
v = f.createVariable('intensity_values', '>f', ('point_number',))
if hasattr(df.values, 'todense'):
v[:] = df.values.todense()[scan_locs]
else:
v[:] = df.values[scan_locs]
# TODO: check that these do anything
# f.createVariable('time_values', 'd', ('point_number',))
v = f.createVariable('resolution', 'd', ('scan_number',))
v[:] = -9999
f.createVariable('actual_scan_number', 'i', ('scan_number',))
v[:] = -9999
f.createVariable('scan_index', 'i', ('scan_number',))
v[:] = np.cumsum(np.sum(scan_locs, axis=1).astype('i'))
f.createVariable('mass_range_min', 'd', ('scan_number',))
v[:] = np.min(stretch_t[scan_locs]) * np.ones(stretch_t.shape[0])
v = f.createVariable('mass_range_max', 'd', ('scan_number',))
v[:] = np.max(stretch_t[scan_locs]) * np.ones(stretch_t.shape[0])
v = f.createVariable('a_d_sampling_rate', 'd', ('scan_number',))
v[:] = -9999
v = f.createVariable('a_d_coaddition_factor', 'h', ('scan_number',))
v[:] = -9999
v = f.createVariable('flag_count', 'i', ('scan_number',))
v[:] = 0
f.createVariable('inter_scan_time', 'd', ('scan_number',))
v[0] = 0
v[1:] = np.diff(df.index.astype('d'))
f.createVariable('scan_duration', 'd', ('scan_number',))
v[:] = 1
v = f.createVariable('time_range_min', 'd', ('scan_number',))
v[:] = -9999
v = f.createVariable('time_range_max', 'd', ('scan_number',))
v[:] = -9999
inst_tup = ('instrument_number', '_32_byte_string')
f.createVariable('instrument_serial_no', 'c', inst_tup)
f.createVariable('instrument_fw_version', 'c', inst_tup)
f.createVariable('instrument_app_version', 'c', inst_tup)
f.createVariable('instrument_os_version', 'c', inst_tup)
f.createVariable('instrument_sw_version', 'c', inst_tup)
f.createVariable('instrument_comments', 'c', inst_tup)
f.createVariable('instrument_model', 'c', inst_tup)
f.createVariable('instrument_name', 'c', inst_tup)
f.createVariable('instrument_id', 'c', inst_tup)
f.createVariable('instrument_mfr', 'c', inst_tup)
f.close()
